Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework
Abstract In the context of the propagation of infectious diseases, when a sufficient degree of immunisation is achieved within a population, the spread of the disease is ended or significantly decreased, leading to collective immunity, meaning the indirect protection given by immune individuals to s...
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| Autor*in: |
Lima, Isaías [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Natural computing - Springer Netherlands, 2002, 21(2022), 3 vom: 18. Juni, Seite 449-461 |
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| Übergeordnetes Werk: |
volume:21 ; year:2022 ; number:3 ; day:18 ; month:06 ; pages:449-461 |
| Links: |
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| DOI / URN: |
10.1007/s11047-022-09893-3 |
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OLC2079438727 |
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| 520 | |a Abstract In the context of the propagation of infectious diseases, when a sufficient degree of immunisation is achieved within a population, the spread of the disease is ended or significantly decreased, leading to collective immunity, meaning the indirect protection given by immune individuals to susceptible individuals. Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via a Moore neighbourhood of radius one, with a view to analyze the impact of initially immune individuals on the dynamics of COVID-19. This impact was measured by comparing a progression of initial immunity ratio—the percentage of immunised individuals before patient zero starts infecting its neighbourhood—from 0 to 95% of the initial population, with the number of susceptible individuals not contaminated, the peak value of active cases, the total number of deaths and the emulated pandemic duration in days. The influence of this range of immunities over the model was tested with different parameterisations regarding the uncertainties involved in the model such as the durations of the cellular automaton states, the contamination contributions of each state and the state transition probabilities. A collective immunity threshold of $$55\% \pm 2.5\%$$ on average was obtained from this procedure, under four distinct parameterisations, which is in tune with the estimates of the currently available medical literature, even increasing the uncertainty of the input parameters. | ||
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10.1007/s11047-022-09893-3 doi (DE-627)OLC2079438727 (DE-He213)s11047-022-09893-3-p DE-627 ger DE-627 rakwb eng 570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Lima, Isaías verfasserin (orcid)0000-0003-0407-4683 aut Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract In the context of the propagation of infectious diseases, when a sufficient degree of immunisation is achieved within a population, the spread of the disease is ended or significantly decreased, leading to collective immunity, meaning the indirect protection given by immune individuals to susceptible individuals. Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via a Moore neighbourhood of radius one, with a view to analyze the impact of initially immune individuals on the dynamics of COVID-19. This impact was measured by comparing a progression of initial immunity ratio—the percentage of immunised individuals before patient zero starts infecting its neighbourhood—from 0 to 95% of the initial population, with the number of susceptible individuals not contaminated, the peak value of active cases, the total number of deaths and the emulated pandemic duration in days. The influence of this range of immunities over the model was tested with different parameterisations regarding the uncertainties involved in the model such as the durations of the cellular automaton states, the contamination contributions of each state and the state transition probabilities. A collective immunity threshold of $$55\% \pm 2.5\%$$ on average was obtained from this procedure, under four distinct parameterisations, which is in tune with the estimates of the currently available medical literature, even increasing the uncertainty of the input parameters. Stochastic cellular automaton COVID-19 SARS-CoV-2 Contagious disease dynamic collective immunity Balbi, Pedro Paulo aut Enthalten in Natural computing Springer Netherlands, 2002 21(2022), 3 vom: 18. Juni, Seite 449-461 (DE-627)36367487X (DE-600)2110258-2 (DE-576)9363674878 1567-7818 nnns volume:21 year:2022 number:3 day:18 month:06 pages:449-461 https://doi.org/10.1007/s11047-022-09893-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 54.28$jNichtelektronische Datenverarbeitung VZ 106418858 (DE-625)106418858 54.72$jKünstliche Intelligenz VZ 10641240X (DE-625)10641240X AR 21 2022 3 18 06 449-461 |
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570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework Stochastic cellular automaton COVID-19 SARS-CoV-2 Contagious disease dynamic collective immunity |
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Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework |
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estimates of the collective immunity to covid-19 derived from a stochastic cellular automaton based framework |
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Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework |
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Abstract In the context of the propagation of infectious diseases, when a sufficient degree of immunisation is achieved within a population, the spread of the disease is ended or significantly decreased, leading to collective immunity, meaning the indirect protection given by immune individuals to susceptible individuals. Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via a Moore neighbourhood of radius one, with a view to analyze the impact of initially immune individuals on the dynamics of COVID-19. This impact was measured by comparing a progression of initial immunity ratio—the percentage of immunised individuals before patient zero starts infecting its neighbourhood—from 0 to 95% of the initial population, with the number of susceptible individuals not contaminated, the peak value of active cases, the total number of deaths and the emulated pandemic duration in days. The influence of this range of immunities over the model was tested with different parameterisations regarding the uncertainties involved in the model such as the durations of the cellular automaton states, the contamination contributions of each state and the state transition probabilities. A collective immunity threshold of $$55\% \pm 2.5\%$$ on average was obtained from this procedure, under four distinct parameterisations, which is in tune with the estimates of the currently available medical literature, even increasing the uncertainty of the input parameters. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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Abstract In the context of the propagation of infectious diseases, when a sufficient degree of immunisation is achieved within a population, the spread of the disease is ended or significantly decreased, leading to collective immunity, meaning the indirect protection given by immune individuals to susceptible individuals. Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via a Moore neighbourhood of radius one, with a view to analyze the impact of initially immune individuals on the dynamics of COVID-19. This impact was measured by comparing a progression of initial immunity ratio—the percentage of immunised individuals before patient zero starts infecting its neighbourhood—from 0 to 95% of the initial population, with the number of susceptible individuals not contaminated, the peak value of active cases, the total number of deaths and the emulated pandemic duration in days. The influence of this range of immunities over the model was tested with different parameterisations regarding the uncertainties involved in the model such as the durations of the cellular automaton states, the contamination contributions of each state and the state transition probabilities. A collective immunity threshold of $$55\% \pm 2.5\%$$ on average was obtained from this procedure, under four distinct parameterisations, which is in tune with the estimates of the currently available medical literature, even increasing the uncertainty of the input parameters. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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Abstract In the context of the propagation of infectious diseases, when a sufficient degree of immunisation is achieved within a population, the spread of the disease is ended or significantly decreased, leading to collective immunity, meaning the indirect protection given by immune individuals to susceptible individuals. Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via a Moore neighbourhood of radius one, with a view to analyze the impact of initially immune individuals on the dynamics of COVID-19. This impact was measured by comparing a progression of initial immunity ratio—the percentage of immunised individuals before patient zero starts infecting its neighbourhood—from 0 to 95% of the initial population, with the number of susceptible individuals not contaminated, the peak value of active cases, the total number of deaths and the emulated pandemic duration in days. The influence of this range of immunities over the model was tested with different parameterisations regarding the uncertainties involved in the model such as the durations of the cellular automaton states, the contamination contributions of each state and the state transition probabilities. A collective immunity threshold of $$55\% \pm 2.5\%$$ on average was obtained from this procedure, under four distinct parameterisations, which is in tune with the estimates of the currently available medical literature, even increasing the uncertainty of the input parameters. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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